from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2021-12-14 14:03:49.112499
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 14, Dec, 2021
Time: 14:03:54
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -47.4933
Nobs: 505.000 HQIC: -47.9509
Log likelihood: 5823.08 FPE: 1.11423e-21
AIC: -48.2462 Det(Omega_mle): 9.33967e-22
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.362816 0.079517 4.563 0.000
L1.Burgenland 0.098014 0.044003 2.227 0.026
L1.Kärnten -0.115756 0.022643 -5.112 0.000
L1.Niederösterreich 0.175279 0.091193 1.922 0.055
L1.Oberösterreich 0.129272 0.092399 1.399 0.162
L1.Salzburg 0.282286 0.047302 5.968 0.000
L1.Steiermark 0.021414 0.061035 0.351 0.726
L1.Tirol 0.107296 0.049330 2.175 0.030
L1.Vorarlberg -0.084294 0.043478 -1.939 0.053
L1.Wien 0.029938 0.083013 0.361 0.718
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.017241 0.176009 0.098 0.922
L1.Burgenland -0.051167 0.097401 -0.525 0.599
L1.Kärnten 0.035862 0.050120 0.716 0.474
L1.Niederösterreich -0.214280 0.201854 -1.062 0.288
L1.Oberösterreich 0.470372 0.204525 2.300 0.021
L1.Salzburg 0.313667 0.104702 2.996 0.003
L1.Steiermark 0.103667 0.135099 0.767 0.443
L1.Tirol 0.311074 0.109192 2.849 0.004
L1.Vorarlberg 0.007490 0.096239 0.078 0.938
L1.Wien 0.016544 0.183747 0.090 0.928
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.219535 0.040498 5.421 0.000
L1.Burgenland 0.091569 0.022411 4.086 0.000
L1.Kärnten -0.005090 0.011532 -0.441 0.659
L1.Niederösterreich 0.225590 0.046445 4.857 0.000
L1.Oberösterreich 0.167191 0.047059 3.553 0.000
L1.Salzburg 0.037211 0.024091 1.545 0.122
L1.Steiermark 0.027235 0.031085 0.876 0.381
L1.Tirol 0.076880 0.025124 3.060 0.002
L1.Vorarlberg 0.054835 0.022144 2.476 0.013
L1.Wien 0.106385 0.042279 2.516 0.012
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.161538 0.039577 4.082 0.000
L1.Burgenland 0.041932 0.021901 1.915 0.056
L1.Kärnten -0.012705 0.011270 -1.127 0.260
L1.Niederösterreich 0.152065 0.045388 3.350 0.001
L1.Oberösterreich 0.344552 0.045989 7.492 0.000
L1.Salzburg 0.100949 0.023543 4.288 0.000
L1.Steiermark 0.108931 0.030378 3.586 0.000
L1.Tirol 0.087215 0.024552 3.552 0.000
L1.Vorarlberg 0.053232 0.021640 2.460 0.014
L1.Wien -0.038196 0.041317 -0.924 0.355
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.153548 0.075892 2.023 0.043
L1.Burgenland -0.040245 0.041998 -0.958 0.338
L1.Kärnten -0.036415 0.021611 -1.685 0.092
L1.Niederösterreich 0.131043 0.087036 1.506 0.132
L1.Oberösterreich 0.189137 0.088188 2.145 0.032
L1.Salzburg 0.255980 0.045146 5.670 0.000
L1.Steiermark 0.074929 0.058253 1.286 0.198
L1.Tirol 0.130945 0.047082 2.781 0.005
L1.Vorarlberg 0.104100 0.041497 2.509 0.012
L1.Wien 0.039990 0.079229 0.505 0.614
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.078994 0.060098 1.314 0.189
L1.Burgenland 0.016090 0.033257 0.484 0.629
L1.Kärnten 0.051202 0.017113 2.992 0.003
L1.Niederösterreich 0.182276 0.068923 2.645 0.008
L1.Oberösterreich 0.337699 0.069835 4.836 0.000
L1.Salzburg 0.050200 0.035751 1.404 0.160
L1.Steiermark -0.006224 0.046130 -0.135 0.893
L1.Tirol 0.124334 0.037283 3.335 0.001
L1.Vorarlberg 0.057851 0.032861 1.761 0.078
L1.Wien 0.109177 0.062740 1.740 0.082
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.167974 0.072929 2.303 0.021
L1.Burgenland 0.012903 0.040358 0.320 0.749
L1.Kärnten -0.060889 0.020767 -2.932 0.003
L1.Niederösterreich -0.112046 0.083638 -1.340 0.180
L1.Oberösterreich 0.232571 0.084744 2.744 0.006
L1.Salzburg 0.038817 0.043383 0.895 0.371
L1.Steiermark 0.263437 0.055978 4.706 0.000
L1.Tirol 0.489542 0.045243 10.820 0.000
L1.Vorarlberg 0.070256 0.039876 1.762 0.078
L1.Wien -0.099781 0.076135 -1.311 0.190
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.143364 0.080665 1.777 0.076
L1.Burgenland -0.012428 0.044639 -0.278 0.781
L1.Kärnten 0.063455 0.022970 2.762 0.006
L1.Niederösterreich 0.170874 0.092510 1.847 0.065
L1.Oberösterreich -0.078134 0.093734 -0.834 0.405
L1.Salzburg 0.223750 0.047985 4.663 0.000
L1.Steiermark 0.134630 0.061917 2.174 0.030
L1.Tirol 0.052417 0.050043 1.047 0.295
L1.Vorarlberg 0.140738 0.044107 3.191 0.001
L1.Wien 0.165143 0.084212 1.961 0.050
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.457244 0.044707 10.228 0.000
L1.Burgenland -0.000737 0.024740 -0.030 0.976
L1.Kärnten -0.013782 0.012731 -1.083 0.279
L1.Niederösterreich 0.178001 0.051271 3.472 0.001
L1.Oberösterreich 0.262762 0.051950 5.058 0.000
L1.Salzburg 0.019673 0.026595 0.740 0.459
L1.Steiermark -0.010899 0.034316 -0.318 0.751
L1.Tirol 0.071528 0.027735 2.579 0.010
L1.Vorarlberg 0.055877 0.024445 2.286 0.022
L1.Wien -0.018036 0.046672 -0.386 0.699
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.027310 0.092099 0.154123 0.139796 0.065245 0.080506 0.013376 0.208246
Kärnten 0.027310 1.000000 -0.035178 0.130486 0.049175 0.073859 0.455872 -0.080918 0.097010
Niederösterreich 0.092099 -0.035178 1.000000 0.281158 0.099753 0.252430 0.051590 0.143031 0.249098
Oberösterreich 0.154123 0.130486 0.281158 1.000000 0.193828 0.284151 0.160241 0.125937 0.185662
Salzburg 0.139796 0.049175 0.099753 0.193828 1.000000 0.120057 0.061430 0.109287 0.067041
Steiermark 0.065245 0.073859 0.252430 0.284151 0.120057 1.000000 0.132690 0.088790 0.007251
Tirol 0.080506 0.455872 0.051590 0.160241 0.061430 0.132690 1.000000 0.064166 0.127109
Vorarlberg 0.013376 -0.080918 0.143031 0.125937 0.109287 0.088790 0.064166 1.000000 -0.009845
Wien 0.208246 0.097010 0.249098 0.185662 0.067041 0.007251 0.127109 -0.009845 1.000000